FasterLivePortrait
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Fasterliveportrait
Overview :
FasterLivePortrait is a real-time portrait animation project based on deep learning. It achieves real-time running speed of 30+ FPS, including pre-processing and post-processing, by using TensorRT on the RTX 3090 GPU. The project also implements the conversion of the LivePortrait model to an Onnx model and achieves a >70ms/frame inference speed using onnxruntime-gpu on the RTX 3090, supporting cross-platform deployment. In addition, the project supports native gradio apps, enhancing inference speed by several times and supporting simultaneous inference for multiple faces. The code structure has been restructured, no longer relying on PyTorch, all models use onnx or tensorrt for inference.
Target Users :
Purpose:\n\nThe target audience for FasterLivePortrait is primarily deep learning developers, image processing researchers, and professionals in related fields. They need to animate portraits in real-time or deploy deep learning models across platforms. FasterLivePortrait provides efficient inference speed and flexible deployment options suited for developers who require high performance and high compatibility.
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Use Cases
Real-time animation of portraits during video conferences
Static portrait photos turned into dynamic videos for sharing on social media
Real-time generation of dynamic character portraits in games or virtual reality applications
Features
Achieves real-time running speed of 30+ FPS using TensorRT on the RTX 3090 GPU
Converts the LivePortrait model to an Onnx model and supports cross-platform deployment
Supports native gradio apps, enhancing inference speed by several times and supporting simultaneous inference for multiple faces
Restructures the code to no longer rely on PyTorch, using onnx or tensorrt for inference
Supports Docker environment and provides a runnable image
Supports Windows and MacOS integrated packages and provides one-click operation
Supports onnxruntime and TensorRT inference and provides detailed installation and usage instructions
How to Use
1. Install Docker and download the FasterLivePortrait Docker image
2. Run the FasterLivePortrait container using Docker commands
3. Download and convert the Onnx model file and place it in the checkpoint folder
4. Install and configure onnxruntime-gpu or TensorRT
5. Use the provided script to convert the Onnx model to a TensorRT model
6. Run app.py to start the gradio app and select the onnx or tensorrt mode
7. Access the local server (default port: 9870) and use the app for real-time portrait animation
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